task2

task2/scripts/build_cuda.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+cd "$(dirname "$0")/.."
+CUDA_ARCH="${CUDA_ARCH:-sm_35}"
+mkdir -p bin
+nvcc -ccbin g++ -O3 -arch="$CUDA_ARCH" -o bin/wave_cuda src/wave_cuda.cu
+echo "Built bin/wave_cuda (arch=$CUDA_ARCH)"

task2/scripts/build_mpi.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+cd "$(dirname "$0")/.."
+mkdir -p bin
+mpicc -O3 -std=c99 -o bin/wave_mpi src/wave_mpi.c
+echo "Built bin/wave_mpi"

task2/scripts/plot_task2_results.py

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+#!/usr/bin/env python3
+"""
+Строит график зависимости времени вычисления от размера полигона
+для MPI (1, 2, 4 узла) и CUDA.
+
+Использование:
+    python3 plot_task2_results.py \
+        --mpi1 results/task2-mpi-1n-XXXXX.csv \
+        --mpi2 results/task2-mpi-2n-XXXXX.csv \
+        --mpi4 results/task2-mpi-4n-XXXXX.csv \
+        --cuda results/task2-cuda-XXXXX.csv \
+        -o report/img/task2-time-comparison.png
+"""
+import argparse
+import csv
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+
+
+def read_mpi_csv(path: str) -> tuple[list[int], list[float]]:
+    sizes, times = [], []
+    with open(path) as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            sizes.append(int(row["n"]))
+            times.append(float(row["time_ms"]))
+    return sizes, times
+
+
+def read_cuda_csv(path: str) -> tuple[list[int], list[float]]:
+    sizes, times = [], []
+    with open(path) as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            sizes.append(int(row["n"]))
+            times.append(float(row["time_ms"]))
+    return sizes, times
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--mpi1", required=True, help="CSV for MPI 1 node")
+    parser.add_argument("--mpi2", required=True, help="CSV for MPI 2 nodes")
+    parser.add_argument("--mpi4", required=True, help="CSV for MPI 4 nodes")
+    parser.add_argument("--cuda", required=True, help="CSV for CUDA")
+    parser.add_argument("-o", "--output", default="task2-time-comparison.png")
+    args = parser.parse_args()
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+
+    for label, path in [
+        ("MPI 1 node", args.mpi1),
+        ("MPI 2 nodes", args.mpi2),
+        ("MPI 4 nodes", args.mpi4),
+        ("CUDA", args.cuda),
+    ]:
+        sizes, times = read_mpi_csv(path) if "mpi" in label.lower() else read_cuda_csv(path)
+        ax.plot(sizes, times, marker="o", label=label)
+
+    ax.set_xlabel("Размер полигона n")
+    ax.set_ylabel("Время, мс")
+    ax.set_title("Зависимость времени вычисления от размера полигона")
+    ax.legend()
+    ax.grid(True, alpha=0.3)
+
+    Path(args.output).parent.mkdir(parents=True, exist_ok=True)
+    fig.savefig(args.output, dpi=150, bbox_inches="tight")
+    print(f"Saved: {args.output}")
+
+
+if __name__ == "__main__":
+    main()

task2/scripts/run_cuda.slurm

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+#!/usr/bin/env bash
+#SBATCH --job-name=task2-cuda
+#SBATCH --partition=tornado-k40
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --time=00:20:00
+#SBATCH --output=results/%x-%j.out
+#SBATCH --error=results/%x-%j.err
+
+set -euo pipefail
+
+cd "${SLURM_SUBMIT_DIR}"
+
+module purge
+module load compiler/gcc/11
+module load nvidia/cuda/11.6u2
+
+mkdir -p results bin
+
+./scripts/build_cuda.sh
+
+echo "===== account info ====="
+whoami; hostname; date
+
+echo
+echo "===== slurm info ====="
+echo "SLURM_JOB_ID=${SLURM_JOB_ID:-unknown}"
+echo "SLURM_JOB_PARTITION=${SLURM_JOB_PARTITION:-unknown}"
+echo "SLURM_NODELIST=${SLURM_NODELIST:-unknown}"
+scontrol show job "${SLURM_JOB_ID}" || true
+
+echo
+echo "===== node config ====="
+lscpu | head -20
+nvidia-smi -L || true
+nvidia-smi || true
+
+CSV="results/task2-cuda-${SLURM_JOB_ID}.csv"
+echo "n,impl,time_ms,path_len,iterations" > "$CSV"
+
+echo
+echo "===== benchmark ====="
+for N in 500 1000 2000 3000 5000; do
+    echo "--- n=$N ---"
+    ./bin/wave_cuda "$N" 256 256 "$CSV"
+done
+
+echo
+echo "===== done ====="

task2/scripts/run_mpi.slurm

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+#!/usr/bin/env bash
+#SBATCH --job-name=task2-mpi
+#SBATCH --partition=tornado
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=56
+#SBATCH --time=00:20:00
+#SBATCH --output=results/%x-%j.out
+#SBATCH --error=results/%x-%j.err
+
+set -euo pipefail
+
+cd "${SLURM_SUBMIT_DIR}"
+
+module purge
+module load compiler/gcc/11
+module load mpi/openmpi
+
+mkdir -p results bin
+
+./scripts/build_mpi.sh
+
+RANKS=${SLURM_JOB_NUM_NODES}
+
+echo "===== account info ====="
+whoami; hostname; date
+
+echo
+echo "===== slurm info ====="
+echo "SLURM_JOB_ID=${SLURM_JOB_ID:-unknown}"
+echo "SLURM_JOB_PARTITION=${SLURM_JOB_PARTITION:-unknown}"
+echo "SLURM_JOB_NUM_NODES=${SLURM_JOB_NUM_NODES:-unknown}"
+echo "SLURM_NODELIST=${SLURM_NODELIST:-unknown}"
+echo "RANKS=${RANKS}"
+scontrol show job "${SLURM_JOB_ID}" || true
+
+echo
+echo "===== node config ====="
+lscpu | head -20
+if [ -n "${SLURMD_NODENAME:-}" ]; then
+    scontrol show node "${SLURMD_NODENAME}" || true
+fi
+
+CSV="results/task2-mpi-${RANKS}n-${SLURM_JOB_ID}.csv"
+echo "n,procs,time_ms,path_len,iterations" > "$CSV"
+
+echo
+echo "===== benchmark (${RANKS} nodes / ${RANKS} ranks) ====="
+for N in 500 1000 2000 3000 5000; do
+    echo "--- n=$N ---"
+    mpirun -np "${RANKS}" --map-by ppr:1:node --bind-to none ./bin/wave_mpi "$N" "$CSV"
+done
+
+echo
+echo "===== done ====="